Welcome to Tidyverse

ANU BDSI
workshop Data Wrangling with R Part 1

Emi Tanaka

Biological Data Science Institute

8th April 2024

Current learning objectives

  • -Recognize the characteristics of tidy data
  • Differentiate between the Base and Tidyverse paradigms
  • Acquire the skills to add/modify columns, subset data by rows and columns, rename column names, and perform group operations using dplyr
  • -Pivot data into longer or wider format using tidyr
  • -Join datasets using dplyr

Base R

  • R has 7 packages, collectively referred to as the “base R”, that are loaded automatically when you launch it.
  • The functions in the base packages are generally well-tested and trustworthy.

Tidyverse

  • Tidyverse refers to a collection of R-packages that share a common (opinionated) design philosophy, grammar and data structure.
  • This trains your mental model to do data science tasks in a manner which may make it easier, faster, and/or fun for you to do these tasks.
  • library(tidyverse) is a shorthand for loading the 9 core tidyverse packages.
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.0     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Data wrangling with Base R

data.frame (Base R)

  • In R, data.frame is a special class of list.
  • Every column in the data.frame is a vector of the same length.
  • Each entry in a vector have the same type, e.g. logical, integer, double (or numeric), character or factor.
  • It has an attribute row.names which could be a sequence of integers indexing the row number or some unique identifier.
mtcars
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

list (Base R)

Subsetting by column (Base R)

  • Remember, data.frame is just a special type of list and inherit methods applied for list.
  • data.frame also can be accessed by array index:

Subsetting by column (Base R)

  • data.frame inherits methods for list, but list does not inherit methods for data.frame.
  • If you subset a single column using [, ], then by default the output is a vector and not a data.frame.

Subsetting by row (Base R)

Adding or modifying a column (Base R)

Adding a row (Base R)

What do you notice with the order of the new entry?

Sorting columns (Base R)

Sorting rows (Base R)

Calculating statistical summaries by group (Base R)

🎯 Calculate the average weight (wt) of a car for each gear type in (gear) mtcars

🎯 Calculate the median weight (wt) of a car for each gear (gear) and engine (vs) type in mtcars

Data wrangling with Tidyverse

A grammar of data manipulation

  • dplyr is a core package in tidyverse
  • The earlier concept of dplyr (first on CRAN in 2014-01-29) was implemented in plyr (first on CRAN in 2008-10-08)
  • The functions in dplyr has been evolving frequently but dplyr v1.0.0 was released on CRAN in 2020-05-29
  • This new version contained new “verbs”
  • The major release suggests that functions in dplyr are maturing and thus the user interface is unlikely to change

dplyr “verbs”

  • The main functions of dplyr include:
    • arrange
    • select
    • mutate
    • rename
    • group_by
    • summarise
  • Notice that these functions are verbs

dplyr structure

  • Functions in dplyr generally have the form:
verb(data, args)

  • I.e., the first argument data is a data.frame object

  • What do you think the following will do?

Base R and Tidyverse

  • Tidyverse is not a substitute for Base R
  • Knowing Base R is essential to use Tidyverse effectively

Summary

Exercise time

15:00

anu-bdsi.github.io/workshop-data-wrangling-R1/